vb_cs_wish: Cross-sectional FoSR using Variational Bayes and Wishart...
In jeff-goldsmith/BayesFoSR: Bayesian Function-on-Scalar Regression

Description Usage Arguments Author(s) References

Fitting function for function-on-scalar regression for cross-sectional data. This function estimates model parameters using VB and estimates the residual covariance surface using a Wishart prior.

1 2	vb_cs_wish(formula, data = NULL, verbose = TRUE, Kt = 5, alpha = 0.1, min.iter = 10, max.iter = 50, Aw = NULL, Bw = NULL, v = NULL)

`formula`	a formula indicating the structure of the proposed model.
`data`	an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.
`verbose`	logical defaulting to `TRUE` – should updates on progress be printed?
`Kt`	number of spline basis functions used to estimate coefficient functions
`alpha`	tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
`min.iter`	minimum number of interations of VB algorithm
`max.iter`	maximum number of interations of VB algorithm
`Aw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects; if `NULL`, defaults to `Kt/2`.
`Bw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects; if `NULL`, defaults to 1/2 tr(mu.q.beta of the model
`v`	hyperparameter for inverse Wishart prior on residual covariance; if `NULL`, Psi defaults to an FPCA decomposition of the residual covariance in which residuals are estimated based on an OLS fit of the model (note the "nugget effect" on this covariance is assumed to be constant over the time domain).